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songsee

Generate spectrograms and audio feature visualizations from audio files via CLI.

Updated Jun 5, 2026
Search Tags:#cli#visualization#music#spectrogram#mfcc#audio analysis#mel
One-click install
npx skills add https://github.com/xu1713/openhorse --skill songsee-xu1713
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: songsee
Source: https://github.com/xu1713/openhorse/tree/main/openhorse/openhorse/skills/media/songsee
Command: npx skills add https://github.com/xu1713/openhorse --skill songsee-xu1713

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Analyzing audio content visually requires specialized tooling; this Skill turns audio files into spectrograms and multi-panel feature visualizations (mel, chroma, MFCC, and more) with a single CLI command, making audio structure, tempo, and spectral content inspectable as images. ## Core Features & Use Cases - Spectrogram Generation: Render standard or mel-scaled spectrograms from WAV and MP3 files, with ffmpeg support for other formats. - Multi-Panel Feature Grids: Combine up to nine visualization types (chroma, hpss, self-similarity, loudness, tempogram, MFCC, spectral flux) into a single image. - Flexible Output Control: Slice audio by time range, choose color palettes, set image dimensions, and output PNG or JPG. - Use Case: Compare two synthesized audio outputs by generating side-by-side mel spectrograms, then inspect the images with a vision model to verify frequency content and detect artifacts. ## Quick Start Generate a mel spectrogram from the attached audio file 'track.mp3' and save it as a PNG image.

Frequently Asked Questions about songsee

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a spectrogram from an MP3 file?▼

Run songsee with the audio file as argument, for example 'songsee track.mp3 -o spectrogram.png'. WAV and MP3 are decoded natively, and the output can be saved as PNG or JPG with configurable dimensions and color palettes.

What audio visualization types does songsee support?▼

songsee supports nine visualization types: spectrogram, mel, chroma, hpss, self-similarity, loudness, tempogram, MFCC, and spectral flux. Pass them as comma-separated values to the --viz flag to render multiple panels in a single grid image.

Does songsee support audio formats other than WAV and MP3?▼

WAV and MP3 are decoded natively without extra tools. For other formats such as FLAC or AAC, you need ffmpeg installed so songsee can decode them before visualization.

How do I visualize only part of an audio file?▼

Use the --start and --duration flags to select a time slice, for example 'songsee track.mp3 --start 12.5 --duration 8' renders an 8-second segment starting at 12.5 seconds. You can also pipe audio via stdin.

Why does the songsee command fail after installation?▼

The most common cause is a missing Go installation or the binary not being on your PATH. Install Go first, then run 'go install github.com/steipete/songsee/cmd/songsee@latest' and ensure your Go bin directory is in PATH.

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